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Keywords: Feature Selection
MO-E115-IePD-F6-6A Hybrid Model Derived From Multi-Parametric MRIs for Predicting Neoadjuvant Chemoradiation Response in Locally Advanced Rectal Cancer
Hao CHEN1*, Xing Li2, X. Sharon Qi3, (1) Xi'an University of Posts and telecommunications, Xi'an, shaanxi, CN, (2) Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, CN, (3) UCLA School of Medicine, Los Angeles, CA
PO-GePV-M-60Assessing Role of Feature Selection in Deep Transfer Classification of Indeterminant Thyroid Nodules
JL Cozzi*, H Li, J Williams, J Conn Busch, L Lan, XM Keutgen, ML Giger, University of Chicago, Chicago, IL
PO-GePV-M-65A Comparative Evaluation of Radiomic Feature Selection and Machine Learning Models for Clinical Outcome Prediction in Lung Cancer
G Ge*, J Zhang, University of Kentucky, Lexington, KY
PO-GePV-M-68Stability of Radiomics Features Using 4D-CT Across Radiomics Platforms and Contour Variations for Lung and Liver Tumors 
X Wang1*, C Ma1, Y Li1,2, Y Zhang1, Y Zhang1, N Yue1, K Nie1, (1) Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, (2) Saint Vincent Hospital, Worchester, MA
PO-GePV-M-346Predicting Overall Survival for Patients with Oropharyngeal Cancer Using An Interpretable Machine Learning Algorithm
X Pan1*, T Feng2, C Liu3, X Qi4, (1) Xi'an University of Posts and Telecommunications, Xi'an, shaanxi, CN, (2) Xi'an University of Posts and Telecommunications, Xi'an, shaanxi, CN, (3) Xi'an University of Posts and Telecommunications, Xi'an, shaanxi, CN, (4) UCLA School of Medicine, Los Angeles, CA
PO-GePV-M-347A Multi-Objective Radiomic Model for Predicting Survival in Patients with Oropharyngeal Cancer
X Pan1*, C Liu2, T Feng3, X Qi4, (1) Xi'an University of Posts and Telecommunications, Xi'an,shaanxi, CN, (2) Xi'an University of Posts and Telecommunications, Xi'an,shaanxi, CN, (3) Xi'an University of Posts and Telecommunications, Xi'an,shaanxi, CN, (4) UCLA School of Medicine, Los Angeles, CA
PO-GePV-T-120A Radiomics-Based Light Gradient Boosting Machine to Predict Radiation-Induced Toxicities in Nasopharynx Cancer Patients Receiving Chemoradiotherapy
Z Jiang1*, Y Liang2, X Wang3, Z Min4, M Feng5, Y Kuang6, (1) University of Nevada, Las Vegas, Las Vegas, NV, (2) Cancer Hospital Chinese Academy of Medical Sciences, Sichuan Center, Chengdu, CN, (3) Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, CN,(4) Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, CN,(5) Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, CN,(6) University of Nevada, Las Vegas, Las Vegas, NV
SU-H400-IePD-F5-1A Graph Attention Network Combining Both Radiomics and Clinical Data for Improved and Interpretable Prediction of Lymph Node Invasion in High-Grade Prostate Cancers
M Larose*1, 2, 3, N Touma3, N Raymond4, D Leblanc1, 2, 3, F Rasekh3, B Neveu3, H Hovington3, M Vallières4, F Pouliot3, L Archambault1, 2, 3, (1) Department of physics, Université Laval, Qc, Canada (2) Centre de recherche sur le cancer, Qc, Canada (3) CHU de Québec, QC, Canada (4) Department of computer science, Université de Sherbrooke, QC, Canada

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